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>51.1. Query Handling as a Complex Optimization Problem</A
></H1
><P
> Among all relational operators the most difficult one to process
and optimize is the <I
CLASS="FIRSTTERM"
>join</I
>. The number of
possible query plans grows exponentially with the
number of joins in the query. Further optimization effort is
caused by the support of a variety of <I
CLASS="FIRSTTERM"
>join
methods</I
> (e.g., nested loop, hash join, merge join in
<SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>) to process individual joins
and a diversity of <I
CLASS="FIRSTTERM"
>indexes</I
> (e.g.,
B-tree, hash, GiST and GIN in <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>) as
access paths for relations.
</P
><P
> The normal <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> query optimizer
performs a <I
CLASS="FIRSTTERM"
>near-exhaustive search</I
> over the
space of alternative strategies. This algorithm, first introduced
in IBM's System R database, produces a near-optimal join order,
but can take an enormous amount of time and memory space when the
number of joins in the query grows large. This makes the ordinary
<SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> query optimizer
inappropriate for queries that join a large number of tables.
</P
><P
> The Institute of Automatic Control at the University of Mining and
Technology, in Freiberg, Germany, encountered some problems when
it wanted to use <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> as the
backend for a decision support knowledge based system for the
maintenance of an electrical power grid. The DBMS needed to handle
large join queries for the inference machine of the knowledge
based system. The number of joins in these queries made using the
normal query optimizer infeasible.
</P
><P
> In the following we describe the implementation of a
<I
CLASS="FIRSTTERM"
>genetic algorithm</I
> to solve the join
ordering problem in a manner that is efficient for queries
involving large numbers of joins.
</P
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